Existing transformation-based image analysis uses direct anatomical information, such as shape, volume, and intensity of anatomical structures. These methods use image contrast to guide the transformation, and therefore, are sensitive to the variation in contrast not only due to the anatomical abnormalities, but also to the differences in scanner and image parameters.
Conventional structural MRI still plays a leading part in clinical diagnostic radiology, providing vast amounts of anatomical information. There are numerous clinical hallmarks and signs that can be depicted by structural MRI, which are well established after more than 30 years of clinical application. Currently, clinical MR images are interpreted by radiologists and stored electronically in the picture archiving and communication system (PACS) with the radiology reports. A text-based image searching of PACS enables the retrieval of stored images with the clinical information and radiology report. This searching capability dramatically improved daily clinical practice by saving time and effort to collect images from a patient to evaluate disease progression and the efficacy of treatments, and to collect images from a specific clinical condition to investigate the common anatomical phenotype depicted by MRI.
However, to further aid in clinical use, an image-based search, in which the patient's image is submitted to PACS as a “keyword,” and past images with similar anatomical phenotypes are identified, and a statistical report about the diagnosis and prognosis is provided, would be far more informative. This type of image searching is called content-based image retrieval (CBIR), which is an anticipated technology in medical imaging (El-Kwae et al., 2000; Greenspan and Pinhas, 2007; Muller et al., 2005; Orphanoudakis et al., 1996; Rahman et al., 2007; Robinson et al., 1996; Sinha et al., 2001; Unay et al., 2010). Although the CBIR is a promising technology, to date, the application to PACS is limited (Muller et al., 2004; Sinha and Kangarloo, 2002), because of the difficulty of extracting features from the stored images, especially for brain MRI, which consists of numerous anatomical structures with highly varying intensity, volume, and shape among diseases and even among normal individuals.
One of the solutions is to apply image quantification technologies, which has been the subject of extensive research in the last two decades (Ashburner and Friston, 1999; Chiang et al., 2008; Good et al., 2001; Mazziotta et al., 2001; Smith et al., 2006; Verma et al., 2005; Wright et al., 1995; Yushkevich et al., 2008; Zhang et al., 2006). These analyses have been mostly designed for traditional group-based studies, in which strict inclusion criteria and age-matched controls were essential, but often incompatible, with clinical practice where an individual image, not a group of diseases, is the target of the analysis.
The concept of group analysis assumes consistent locations of abnormalities, which does not hold for clinical situations with heterogeneous patient populations and image quality. There are diseases with lesions that are not seen in the normal brain, such as stroke and brain tumors, and diseases with atrophy in a specific set of anatomical structures, such as Alzheimer's disease. To localize the disease-related pathological changes seen in brain MRI, transformation-based image analysis methods are often employed. However, the lesions with abnormal intensity or the space-occupying lesions often cause significant misregistration of brain structures after image transformation.
The brain with severe atrophy, such as that seen in Alzheimer's disease, is also problematic in terms of the transformation accuracy. There are methods to overcome such inaccuracy by using specific approaches, such as lesion-masking (Andersen et al., 2010; Ripolles et al., 2012) or a disease-specific template (Liao et al., 2012; Mandal et al., 2012; Wang et al., 2012) (e.g., http://www.loni.ucla.edu/Atlases/), but it is still difficult to quantify various types of diseases in the same methodological framework. In addition, most of these methods use image contrast to guide the transformation, and therefore, are sensitive to the variation in contrast not only due to the anatomical abnormalities, but also to the differences in scanner and image parameters.
What is needed are methods, system, and media that are robust to this inhomogeneity seen in clinical images, and that use information about a structural misregistration and intensity mismatch after image registration to capture the anatomical features, instead of direct anatomical information.